• Journal of Environmental Impact Assessment
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• v.29 no.3
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• pp.157-181
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• 2020

Sediments from rivers, lakes and marine ports serve as end points for pollutants discharged into the water, and at the same time serve as sources of pollutants that are continuously released into the water. Until now, the contaminated sediments have been landfilled or dumped at sea. Landfilling, however, was expensive and dumping at sea was completely banned due to the London Convention. Therefore, this study applied contaminated sedimentation soil of 'Royal Palace Livestock Complex' as soil purification method. Soil remediation methods were applied to pretreatment, composting, soil washing, electrokinetics, and thermal desorption by selecting overseas application cases and domestically applicable application technologies. As a result of surveying the site for pollutant characteristics, Disolved Oxigen (DO), Suspended Solid (SS), Chemical Oxygen Demand (COD), Total Nitrogen (TN), and Total Phosphorus (TP) exceeded the discharged water quality standard, and especially SS, COD, TN, and TP exceeded the standard several tens to several hundred times. Soil showed high concentrations of copper and zinc, which promote the growth of pig feed, and cadmium exceeded 1 standard of Soil Environment Conservation Act. In the pretreatment technology, hydrocyclone was used for particle size separation, and the fine soil was separated by more than 80%. Composting was performed on organic and Total Petroleum Hydrocarbon (TPH) contaminated soils. TPH was treated within the standard of concern, and E. coli was analyzed to be high in organic matter, and the fertilizer specification was satisfied by applying the optimum composting conditions at 70℃, but the organic matter content was lower than the fertilizer specification. As a result of continuous washing test, Cd has 5 levels of residual material in fine soil. Cu and Zn were mostly composed of ion exchange properties (stage 1), carbonates (stage 2), and iron / manganese oxides (stage 3), which facilitate easy separation of contamination. As a result of applying acid dissolution and multi-stage washing step by step, hydrochloric acid, 1.0M, 1: 3, 200rpm, 60min was analyzed as the optimal washing factor. Most of the contaminated sediments were found to satisfy the Soil Environmental Conservation Act's standards. Therefore, as a result of the applicability test of this study, soil with high heavy metal contamination was used as aggregate by applying soil cleaning after pre-treatment. It was possible to verify that it was efficient to use organic and oil-contaminated soil as compost Maturity after exterminating contaminants and E. coli by applying composting.

• Korean Journal of Social Welfare
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• v.56 no.3
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• pp.5-35
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• 2004

This research is about the change of the occupation and the income of the subscriber of the medical expenditure due to the economic influence on them. The data of this study are based on 4,215 of medical cost payers among respondents of the survey on "Health and Retirement", which was the fourth additional research of Korea Labor and Income Panel Survey. The main findings of this study are as follows: First, the average medical cost is 5.5% of the income. The ratio of the medical cost to an earned income is highly different between low-income group and high income group. For the low income group, the medical cost reaches up to 1/3 of the total family income. That proves that the medical cost si a heavy burden on them. The group with the high medical expenditure seems to be supported by their own private property and other family members whenever it is needed. But it doesn't show the exact sources of the property, which includes the fund from the interests and real estates. On the other hand, only 14.4% of the subscribers changed their job status on the 5th year, and 85.6% of those kept their job status until the 5th year from the 4th year. This shows that the amount of the medical cost could be the important factor for them to change their job; for example, it is crucial whether the medical expenditure is over the average rate or not. Furthermore, the change of the occupation caused by the medical cost has the negative influence on the gross income. It makes the economic conditions of the family get worse. Therefore, the health insurance in Korea is lack of the compensational function, which substitutes the family income reduced by the change of the job status due to the high medical cost.

• The Korean Journal of Food And Nutrition
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• v.27 no.1
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• pp.22-30
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• 2014

In this study, purple sweet potato bread was prepared by the addition of 0.005%, 0.010%, 0.015% and 0.020% hemicellulase. It's effect on product quality and consumer evaluation were examined. The results showed that the dough pH and total titratable acidity were not significantly different between samples. In the fermentation power of dough expansion, a 0.015% addition sample was the highest between the samples. The bread pH decreased significantly as hemicellulase was increased, whereas. Bread total titratable acidity was significantly decreased. The addition of hemicellulase samples were significantly higher in specific volume and baking loss than the control sample. The moisture content was not significantly different between samples. In colors, the lightness of the control sample was the highest, the redness of the 0.020% addition sample was the lowest while the yellowness of the control was the lowest. The hardness and the fracturability decreased significantly as hemicellulase was increased. The resilience indicated reverse effects. In consumer evaluation, the color and softness were not significantly different between samples. And the hemicellulase addition of samples was higher in flavor than that of the control sample. The overall acceptability was the highest at 5.67 with a 0.010% addition sample. According to these results, the addition of 0.010% hemicellulase in purple sweet potato bread would be the optimum level.

• Journal of Food Hygiene and Safety
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• v.34 no.5
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• pp.463-472
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• 2019

This study investigated the quality characteristics and antioxidant activities of morning bread containing various amounts of aronia sourdough starter. Morning breads were prepared with different volumes (0% (AS0), 15% (AS1), 25% (AS2), and 35% (AS3)) of aronia sourdough starter based on wheat flour. In AS3 group (35% added group), the specific volume and baking loss rate were the highest but the bread height was the lowest. When the aronia sourdough starter was added up to 25%, the expansion power, specific volume, and bread height were significantly increased (P<0.05). According to the added amount of sourdough starters, the lightness and yellowness of the morning bread were decreased, however, redness was increased (P<0.05). In the rheology analysis, hardness, gumminess, and chewiness were significantly decreased with increasing amounts of aronia sourdough starters (P<0.05). However, cohesiveness was the highest in the AS2 group (25% added group). In consumer preference, the highest scores were shown in AS2 group (25% added group) in color, texture, and appearance. The total polyphenol and DPPH (2,2-diphenyl-1-picrylhydrazyl) radical scavenging ability were both significantly increased along with aronia sourdough content (P<0.05). In conclusion, morning bread with 25% aronia sourdough starter showed the best quality characteristics and antioxidant activities.

• Korean Journal of Food Science and Technology
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• v.53 no.6
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• pp.731-738
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• 2021

This study investigated the physicochemical properties of rice starch isolated from broken rice using alkaline steeping (AKL) and enzymatic digestion (ENZ) methods. Broken rice starch (BRS) by AKL and ENZ possessed crude protein contents (0.6-1.4%) acceptable to commercial products of native starch and belonged to an intermediate amylose rice starch. AKL-BRS and ENZ-BRS showed a typical A-type crystal packing arrangement with small variations in their relative crystallinity. ENZ-BRS exhibited higher gelatinization onset and peak temperatures, and a narrower gelatinization temperature range than AKL-BRS, indicating that annealing occurred in ENZ-BRS. Lower swelling power and solubility were generally observed in the ENZ-BRS. ENZ-BRS also showed slower viscosity development, higher peak and trough viscosities, and lower breakdown, final, and setback viscosities, compared to those in AKL-BRS. These results are ascribed to the annealing phenomenon in ENZ-BRS. Overall, BRS from cheap broken rice using AKL and ENZ could contribute to the expansion of rice starch utilization in food and non-food industries.

• Food Engineering Progress
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• v.21 no.4
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• pp.351-359
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• 2017

This study investigates the quality characteristics of dough and bread added with 6% chestnut shell powder and extruded chestnut shell powder at various conditions. As extrusion process variables, melt temperature ($110^{\circ}C$, $130^{\circ}C$, $150^{\circ}C$) and moisture (25% and 30%) were controlled. Total dietary fiber content was slightly increased in extruded chestnut shell powder group. In the farinogram, absorption was significantly increased in the group of 25% moisture content and 30% moisture content (p<0.05). After 2 hours and 3 hours, the leavening heights of dough for control showed a similar tendency to that of dough with extruded chestnut shell at a melt temperature $150^{\circ}C$ and with moisture content of 25% and 30%. Specific volume was the highest at a control of $3.74{\pm}0.08cc/g$ and extruded chestnut shell powder group was slightly higher than the chestnut shell powder group. Firmness after 1 day on control of $107.42{\pm}14.52g$ was similar to that of the bread with extruded chestnut shell at a temperature of $150^{\circ}C$ and moisture content of 25% for $113.33{\pm}6.17g$. In conclusion, the extrusion-cooking of chestnut shell powder improved the quality characteristics of dough and bread. The optimum combinations of conditions in tested range were melt temperature at $150^{\circ}C$ and moisture content at 25%, and melt temperature at $130^{\circ}C$ and moisture content at 30%.

• Korean Journal of Construction Engineering and Management
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• v.22 no.4
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• pp.59-65
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• 2021

Since the 2016 "Housing Act Partial Amendment" and the "2018 Housing Comprehensive Amendment Plan", interest in the pre sale system and post sale system of apartment houses has been on the rise. In order to compare the advantages and disadvantages of the pre sale system and the post sale system of apartment houses, and to establish the basis for the institutional policy of the post sale system, a questionnaire survey method was used for tenants of the apartment house from the public side, and issues of time and cost. The time series analysis method is intended to suggest an appropriate time for payment of contributions. Accordingly, through a review of existing theories and literature, the post sale system of public and private institutions was organized, and through a questionnaire survey, the path to securing pre sale money, product information of the model house, and the degree of awareness of the effect of the post sale system were investigated. For the post sale fund support and payment method, it is necessary to increase the commercial line for existing financiers from the user's point of view, and it is necessary to operate in consideration of the economic power of the pre sale market by region. Both 60% post sale and 80% post sale have a price range of up to KRW 10 million, and the total interest rate is 5.0%, and the annual interest rate is about 2.8% for 60% post sale, and about 2.1% for 80% post sale, which is lower than the current 3.1%. I need an interest rate. The research is a perception survey targeting a total of 5,213 households in a sample of after sale apartments in public institutions. As the actual values are analyzed using a time series on the effects of market supply and demand and market prices, there is a limit to applying them to prospective residents of private apartments. In addition, to respond to first time tenants, a questionnaire survey was conducted on five complexes that have moved in within the last five years.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.121-142
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• 2023

As the number and weight of imported food are steadily increasing, safety management of imported food to prevent food safety accidents is becoming more important. The Ministry of Food and Drug Safety conducts on-site inspections of foreign food facilities before customs clearance as well as import inspection at the customs clearance stage. However, a data-based safety management plan for imported food is needed due to time, cost, and limited resources. In this study, we tried to increase the efficiency of the on-site inspection by preparing a machine learning prediction model that pre-selects the companies that are expected to fail before the on-site inspection. Basic information of 303,272 foreign food facilities and processing businesses collected in the Integrated Food Safety Information Network and 1,689 cases of on-site inspection information data collected from 2019 to April 2022 were collected. After preprocessing the data of foreign food facilities, only the data subject to on-site inspection were extracted using the foreign food facility_code. As a result, it consisted of a total of 1,689 data and 103 variables. For 103 variables, variables that were '0' were removed based on the Theil-U index, and after reducing by applying Multiple Correspondence Analysis, 49 characteristic variables were finally derived. We build eight different models and perform hyperparameter tuning through 5-fold cross validation. Then, the performance of the generated models are evaluated. The research purpose of selecting companies subject to on-site inspection is to maximize the recall, which is the probability of judging nonconforming companies as nonconforming. As a result of applying various algorithms of machine learning, the Random Forest model with the highest Recall_macro, AUROC, Average PR, F1-score, and Balanced Accuracy was evaluated as the best model. Finally, we apply Kernal SHAP (SHapley Additive exPlanations) to present the selection reason for nonconforming facilities of individual instances, and discuss applicability to the on-site inspection facility selection system. Based on the results of this study, it is expected that it will contribute to the efficient operation of limited resources such as manpower and budget by establishing an imported food management system through a data-based scientific risk management model.

• The Mathematical Education
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• v.62 no.3
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• pp.435-455
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• 2023

This study primarily focused on the development of an Explainable Artificial Intelligence (XAI) model to discern and analyze papers with significant impact in the field of mathematics education. To achieve this, meta-information from 29 domestic and international mathematics education journals was utilized to construct a comprehensive academic research network in mathematics education. This academic network was built by integrating five sub-networks: 'paper and its citation network', 'paper and author network', 'paper and journal network', 'co-authorship network', and 'author and affiliation network'. The Random Forest machine learning model was employed to evaluate the impact of individual papers within the mathematics education research network. The SHAP, an XAI model, was used to analyze the reasons behind the AI's assessment of impactful papers. Key features identified for determining impactful papers in the field of mathematics education through the XAI included 'paper network PageRank', 'changes in citations per paper', 'total citations', 'changes in the author's h-index', and 'citations per paper of the journal'. It became evident that papers, authors, and journals play significant roles when evaluating individual papers. When analyzing and comparing domestic and international mathematics education research, variations in these discernment patterns were observed. Notably, the significance of 'co-authorship network PageRank' was emphasized in domestic mathematics education research. The XAI model proposed in this study serves as a tool for determining the impact of papers using AI, providing researchers with strategic direction when writing papers. For instance, expanding the paper network, presenting at academic conferences, and activating the author network through co-authorship were identified as major elements enhancing the impact of a paper. Based on these findings, researchers can have a clear understanding of how their work is perceived and evaluated in academia and identify the key factors influencing these evaluations. This study offers a novel approach to evaluating the impact of mathematics education papers using an explainable AI model, traditionally a process that consumed significant time and resources. This approach not only presents a new paradigm that can be applied to evaluations in various academic fields beyond mathematics education but also is expected to substantially enhance the efficiency and effectiveness of research activities.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.11
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• pp.471-480
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• 2023

The Korean construction order volume in South Korea grew significantly from 91.3 trillion won in public orders in 2013 to a total of 212 trillion won in 2021, particularly in the private sector. As the size of the domestic and overseas markets grew, the scale and complexity of EPC (Engineering, Procurement, Construction) projects increased, and risk management of project management and ITB (Invitation to Bid) documents became a critical issue. The time granted to actual construction companies in the bidding process following the EPC project award is not only limited, but also extremely challenging to review all the risk terms in the ITB document due to manpower and cost issues. Previous research attempted to categorize the risk terms in EPC contract documents and detect them based on AI, but there were limitations to practical use due to problems related to data, such as the limit of labeled data utilization and class imbalance. Therefore, this study aims to develop an AI model that can categorize the contract terms based on the FIDIC Yellow 2017(Federation Internationale Des Ingenieurs-Conseils Contract terms) standard in detail, rather than defining and classifying risk terms like previous research. A multi-text classification function is necessary because the contract terms that need to be reviewed in detail may vary depending on the scale and type of the project. To enhance the performance of the multi-text classification model, we developed the ELECTRA PLM (Pre-trained Language Model) capable of efficiently learning the context of text data from the pre-training stage, and conducted a four-step experiment to validate the performance of the model. As a result, the ensemble version of the self-developed ITB-ELECTRA model and Legal-BERT achieved the best performance with a weighted average F1-Score of 76% in the classification of 57 contract terms.